Material damage and unscheduled downtime due to failures of robotic manipulators and other mechatronic devices used in automated manufacturing tools, such as robotized material-handling platforms for production of semiconductor devices, are common problems which often represent a significant cost burden to the end-user of the manufacturing tools.
A number of health-monitoring and fault-diagnostic (HMFD) methods have been developed for industrial, automotive and aerospace applications. The existing systems typically implement fault detection to indicate that something is wrong in the monitored system, fault isolation to determine the exact location of the fault, i.e., the component which is faulty, and fault identification to determine the magnitude of the fault.
The isolation and identification tasks together are often referred to as fault diagnosis. Many existing systems implement only the fault detection and isolation stages. Generally, the methods used for HMFD may be classified into two major groups: those which do not utilize a mathematical model of the system subject to monitoring and diagnostics, also referred to as the “plant,” and those which do. The methods which do not use the mathematical model of the plant include physical redundancy, utilization of special sensors, limit checking, spectrum analysis, and logical reasoning.
In the physical redundancy approach, multiple sensors are installed to measure the same physical quantity. Any serious discrepancy between the measurements indicates a sensor fault. With only two parallel sensors, fault isolation may not be possible, however, with three or more sensors, a voting scheme may be formed which isolates the faulty sensor. Physical redundancy usually involves extra hardware cost and extra weight.
Special sensors may be installed explicitly for detection and diagnosis. These may be limit sensors (measuring, e.g., temperature or pressure), which perform limit checking (see below) in hardware. Other special sensors may measure some fault-indicating physical quantity, such as sound, vibration, elongation, etc.
In a limit checking approach, widely used in practice, plant measurements are compared by computer to preset limits. Exceeding the threshold indicates a fault situation. In many systems, there are two levels of limits, the first serving for pre-warning while the second triggering an emergency reaction. Limit checking may be extended to monitoring the time-trend of selected variables. While simple and straightforward, the limit checking approach suffers from two serious drawbacks:
(a) Since the plant variables may vary widely due to normal input variations, the test thresholds need to be set quite conservatively; and
(b) The effect of a single component fault may propagate to many plant variables, setting off a confusing multitude of alarms and making isolation extremely difficult.
Spectrum analysis of plant measurements may also be used for detection and isolation. Most plant variables exhibit a typical frequency spectrum under normal operating conditions; any deviation from this may be an indication of abnormality. Certain types of faults may even have their characteristic signature in the spectrum, facilitating fault isolation.
Logical reasoning techniques form a broad class which are complementary to the methods outlined above in that they are aimed at evaluating the symptoms obtained by detection hardware and software. The simplest techniques include logical rules of the “if-symptom-and-symptom-then-conclusion” type. Each conclusion can, in turn, serve as a symptom in the next rule until the final conclusion is reached. The system may process the information presented by the detection hardware and software, or may interact with a human operator, inquiring from him or her about particular symptoms and guiding him or her through the entire logical process.
Turning now to methods which do use a mathematical model of the plant, these model-based condition-monitoring and fault-diagnostic methods generally rely on the concept of analytical redundancy. In contrast to physical redundancy, where measurements from parallel sensors are compared to each other, sensory measurements are compared to analytically computed values of the respective variable. Such computations use present and/or previous measurements of other variables, and a mathematical plant model describing their nominal relationship to the measured variable. The idea can be extended to the comparison of two analytically generated quantities, obtained from different sets of variables. In either case, the resulting differences, called residuals, are indicative of faults in the system. Another class of model-based methods relies directly on parameter estimation.
The generation of residuals needs to be followed by residual evaluation in order to arrive at detection and isolation decisions. Because of the presence of noise and model errors, the residuals are never zero, even if there is no fault. Therefore the detection decision requires testing the residuals against thresholds, which may be obtained empirically or by theoretical considerations. To facilitate fault isolation, the residual generators are usually designed for isolation enhanced residuals, exhibiting structural or directional properties. The isolation decisions then can be obtained in a structural (Boolean) or directional (geometric) framework, with or without the inclusion of statistical elements.
There are four somewhat overlapping approaches to residual generation in model-based condition monitoring and fault diagnostics: Kalman filter, diagnostic observers, parameter estimation and parity relations.
The prediction error of a Kalman filter can be used as a fault detection residual. Its mean is zero if there is no fault (and disturbance) and becomes nonzero in the presence of faults. Since the innovation sequence is white, statistical tests are relatively easy to construct. However, fault isolation is somewhat awkward with the Kalman filter; one needs to run a bank of “matched filters”, one for each suspected fault and for each possible arrival time, and check which filter output can be matched with the actual observations.
Diagnostic observer innovations also qualify as fault detection residuals. “Unknown input” design techniques may be used to decouple the residuals from a limited number of disturbances. The residual sequence is colored, which makes statistical testing somewhat complicated. The freedom in the design of the observer can be utilized to enhance the residuals for isolation. The dynamics of the fault response can be controlled within certain limits by placing the poles of the observer.
Parameter estimation is a natural approach to the detection and isolation of parametric (multiplicative) faults. A reference model is obtained by first identifying the plant in a fault-free situation. Then the parameters are repeatedly re-identified on-line. Deviations from the reference model serve as a basis for detection and isolation. Parameter estimation may be more reliable than analytical redundancy methods, but it is also more demanding in terms of on-line computation and input excitation requirements.
Parity (consistency) relations are rearranged direct input-output model equations subjected to a linear dynamic transformation. The transformed residuals serve for detection and isolation. The residual sequence is colored, just like in the case of observers. The design freedom provided by the transformation can be used for disturbance decoupling and fault isolation enhancement. Also, the dynamics of the response can be assigned within the limits posed by the requirements of causality and stability.
The health-monitoring and fault-diagnostic methods directly applicable to semiconductor manufacturing systems have generally been limited to a small number of faults, for example, those associated with joint backlash. This may be because additional restrictions, such as variability of faults, unsteady and non-uniform operating conditions and limited availability of component characteristics collected over time exist in this area. The analytical methods described above have been primarily applied to systems that are defined by linear equations and are not directly applicable to systems whose dynamics are non-linear. There are, however, a few examples of robotic system applications using parameter identification, the Kalman filter approach, the use of multiple linear neural network models for robot fault diagnosis, and the use of a diagnostic observer for detecting faults in a simulated electro-hydraulic actuator.
It would be advantageous to provide an improved system for monitoring conditions and diagnosing faults.